Systems and methods for deposit predictions based upon monte carlo analysis

ABSTRACT

Systems and methods may be provided for deposit prediction based upon an example Monte Carlo analysis. The system and methods may include receiving deposit information associated with a plurality of deposits for a deposit account of a financial institution customer, where the deposit information includes a plurality of deposit amounts, and a respective date associated with each of the plurality of deposit amounts; applying a Monte Carlo analysis to at least a portion of the received deposit information; and identifying, based upon the applied Monte Carlo analysis, a largest total monetary amount that can be expected to be deposited within a time interval according to a predetermined confidence level.

FIELD OF THE INVENTION

Aspects of the invention relate generally to deposit predictions, andmore particularly to systems and methods for deposit predictions basedupon historical deposit information.

BACKGROUND OF THE INVENTION

Deposits into Demand Deposit Accounts or other bank accounts can includeone or both of electronic deposits or non-electronic deposits.Electronic deposits are typically ACH credits processed through aclearing house network. Non-electronic deposits include ATM orteller-initiated deposits in the form of paper instruments such aschecks or currencies such as cash.

One or more of financial institutions, financial services companies, andother service providers can provide or initiate one or more servicesbased upon knowledge of a predicted deposit amount and/or date.Accordingly, there is a need in the industry for deposit predictionbased upon available deposit information.

SUMMARY OF THE INVENTION

Embodiments of the invention may provide for deposit prediction basedupon historical deposit information. According to an example embodimentof the invention, there may be a method. The method may includeexecuting computer program instructions by one or more processors for:receiving deposit information associated with a plurality of depositsfor a deposit account of a financial institution customer, where thedeposit information includes a plurality of deposit amounts, and arespective date associated with each of the plurality of depositamounts; applying a Monte Carlo analysis to at least a portion of thereceived deposit information; and identifying, based upon the appliedMonte Carlo analysis, a largest total monetary amount that can beexpected to be deposited within a time interval according to apredetermined confidence level.

According to another example embodiment of the invention, there may be asystem. The system may include a memory that stores computer-executableinstructions, and a processor configured to access the memory. Theprocessor may be further configured to execute the computer-executableinstructions to: receive deposit information associated with a pluralityof deposits for a deposit account of a financial institution customer,where the deposit information includes a plurality of deposit amounts,and a respective date associated with each of the plurality of depositamounts; apply a Monte Carlo analysis to at least a portion of thereceived deposit information; and identify, based upon the applied MonteCarlo analysis, a largest total monetary amount that can be expected tobe deposited within a time interval according to a predeterminedconfidence level.

According to yet another example embodiment of the invention, there maybe a system. The system may include means for receiving depositinformation associated with a plurality of deposits for a depositaccount of a financial institution customer, where the depositinformation includes a plurality of deposit amounts, and a respectivedate associated with each of the plurality of deposit amounts; means forapplying a Monte Carlo analysis to at least a portion of the receiveddeposit information; and means for identifying, based upon the appliedMonte Carlo analysis, a largest total monetary amount that can beexpected to be deposited within a time interval according to apredetermined confidence level.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1A illustrates an overview of an example system for depositprediction, according to an example embodiment of the invention.

FIG. 1B illustrates an example service provider computer that may beimplemented by a plurality of computers, according to an exampleembodiment of the invention.

FIG. 2 illustrates a block diagram of an example deposit predictionprocess based upon template matching, according to an example embodimentof the invention.

FIG. 3 illustrates a flow diagram of an example deposit predictionprocess based upon template matching, according to an example embodimentof the invention.

FIGS. 4A and 4B illustrate example template patterns, according to anexample embodiment of the invention.

FIG. 5 illustrates an example process for identifying one or moredeposit patterns, according to an example embodiment of the invention.

FIG. 6A illustrates example representations of electronic depositinformation, according to an example embodiment of the invention.

FIG. 6B illustrates example representations of count reductions asapplied to the electronic deposit information of FIG. 6A, according toan example embodiment of the invention.

FIG. 7 illustrates a block diagram of an example deposit predictionprocess based upon Monte Carlo analysis, according to an exampleembodiment of the invention.

FIG. 8 illustrates a flow diagram of an example deposit predictionprocess based upon Monte Carlo analysis, according to an exampleembodiment of the invention.

FIG. 9 illustrates a flow diagram for an example Monte Carlo analysis,according to an example embodiment of the invention.

FIG. 10 illustrates a graphical representation illustrating a pluralityof distributions of sums deposited during the target date rangecorresponding to a respective plurality of Monte Carlo iterations,according to an example embodiment of the invention.

DETAILED DESCRIPTION

The invention will now be described more fully hereinafter withreference to the accompanying drawings, in which example embodiments ofthe invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexample embodiments set forth herein; rather, these example embodimentsare provided so that this disclosure will be thorough and complete, andwill fully convey the scope of the invention to those of ordinary skillin the art. Like numbers refer to like elements throughout.

Embodiments of the invention may provide for deposit prediction basedupon available deposit information (e.g., deposit information associatedwith a Demand Deposit Account (DDA) or other bank account). Thepredicted next deposit date(s) and amount(s) may be utilized by one ormore financial institutions, financial services companies, or otherservice providers to provide, initiate, direct, or offer one or moreservices, as described herein.

I. System Overview

FIG. 1A illustrates an example system 100 for deposit prediction,according to an example embodiment of the invention. As shown in FIG.1A, a financial institution computer 110, service provider computer 120,and financial services computer 130 may be in communication with eachother via a network 140, which, as described below, can include one ormore separate or shared private and/or public networks, including theInternet or a publicly switched telephone network. Each of thesecomponents—the financial institution computer 110, the service providercomputer 120, the financial services computer 130, and the network140—will now be discussed in further detail.

First, the financial institution computer 110 may be anyprocessor-driven device, such as, but not limited to, a server computer,a mainframe computer, one or more networked computers, a desktopcomputer, a personal computer, a laptop computer, a mobile computer, orany other processor-based device. In addition to having a processor 116,the financial institution computer 110 may further include a memory 112,input/output (“I/O”) interface(s) 118, and network interface(s) 117. Thememory 112 may be any computer-readable medium, coupled to the processor116, such as RAM, ROM, and/or a removable storage device for storingdata files 115 and a database management system (“DBMS”) to facilitatemanagement of data files 115 and other data stored in the memory 112and/or stored in a separate database 119. The memory 112 may also storevarious program modules, such as an operating system (“OS”) 114 and adeposit information module 113. The OS 114 may be, but is not limitedto, Microsoft Windows®, Apple OSX™, Unix, a mainframe computer operatingsystem (e.g., IBM z/OS, MVS, OS/390 earlier, etc.), or a speciallydesigned operating system. The deposit information module 113 maycomprise computer-executable program instructions or software, includinga dedicated program, for managing, storing, or extracting depositaccount information of one or more financial institution customers.

Still referring to the financial institution computer 110, the I/Ointerface(s) 118 may facilitate communication between the processor 116and various I/O devices, such as a keyboard, mouse, printer, microphone,speaker, monitor, bar code readers/scanners, RFID readers, and the like.The network interface(s) 117 may take any of a number of forms, such as,but not limited to, a network interface card, a modem, a wirelessnetwork card, a cellular network card, and the like. Indeed, thefinancial institution computer 110 can receive deposit information vianetwork interface(s) 117 and additionally transmit deposit information,perhaps to service provider computer 120, via network interface 117. Itwill be appreciated that financial institution computer 110 may beimplemented on a particular machine, which may include a computer thatis designed, customized, configured, or programmed to perform at leastone or more functions of the deposit information module 113, accordingto an example embodiment of the invention.

Second, the service provider computer 120 may be any processor-drivendevice, such as, but not limited to, a server computer, a mainframecomputer, one or more networked computers, a desktop computer, apersonal computer, a laptop computer, a mobile computer, or any otherprocessor-based device. In addition to having a processor 126, theservice provider computer 120 may further include a memory 122,input/output (“I/O”) interface(s) 128, and network interface(s) 127. Thememory 122 may be any computer-readable medium, coupled to the processor126, such as RAM, ROM, and/or a removable storage device for storingdata files 125 and a database management system (“DBMS”) to facilitatemanagement of data files 125 and other data stored in the memory 122and/or stored in a separate database 129. The memory 122 may also storevarious program modules, such as an operating system (“OS”) 124 and adeposit prediction module 123. The OS 124 may be, but is not limited to,Microsoft Windows®, Apple OSX™, Unix, a mainframe computer operatingsystem (e.g., IBM z/OS, MVS, OS/390 earlier, etc.), or a speciallydesigned operating system. The deposit prediction module 123 maycomprise computer-executable program instructions or software, includinga dedicated program, for determining, based upon an analysis of depositinformation, a date and/or amount of one or more next deposits.

Still referring to the service provider computer 120, the I/Ointerface(s) 128 may facilitate communication between the processor 126and various I/O devices, such as a keyboard, mouse, printer, microphone,speaker, monitor, bar code readers/scanners, RFID readers, and the like.The network interface(s) 127 may take any of a number of forms, such as,but not limited to, a network interface card, a modem, a wirelessnetwork card, a cellular network card, and the like. Indeed, the serviceprovider computer 120 can receive deposit information from financialinstitution computer 110 via network interface(s) 127, and additionallytransmit deposit prediction information, perhaps to financial servicescomputer 130, via network interface(s) 127. It will be appreciated thatservice provider computer 120 may be implemented on a particularmachine, which may include a computer that is designed, customized,configured, or programmed to perform at least one or more functions ofthe deposit prediction module 123, according to an example embodiment ofthe invention.

Third, the financial services computer 130 may be any processor-drivendevice, such as, but not limited to, a server computer, a mainframecomputer, one or more networked computers, a desktop computer, apersonal computer, a laptop computer, a mobile computer, or any otherprocessor-based device. In addition to having a processor 136, thefinancial services computer 130 may further include a memory 132,input/output (“I/O”) interface(s) 138, and network interface(s) 137. Thememory 132 may be any computer-readable medium, coupled to the processor136, such as RAM, ROM, and/or a removable storage device for storingdata files 135 and a database management system (“DBMS”) to facilitatemanagement of data files 135 and other data stored in the memory 132and/or stored in a separate database 139. The memory 132 may also storevarious program modules, such as an operating system (“OS”) 134 and afinancial application module 133. The OS 134 may be, but is not limitedto, Microsoft Windows®, Apple OSX™, Unix, a mainframe computer operatingsystem (e.g., IBM z/OS, MVS, OS/390 earlier, etc.), or a speciallydesigned operating system. The financial application module 133 maycomprise computer-executable program instructions or software, includinga dedicated program, for determining, based upon a date and/or amount ofone or more next deposits, whether or the extent to which to provide,initiate, or offer one or more services.

Still referring to the financial services computer 130, the I/Ointerface(s) 138 may facilitate communication between the processor 136and various I/O devices, such as a keyboard, mouse, printer, microphone,speaker, monitor, bar code readers/scanners, RFID readers, and the like.The network interface(s) 137 may take any of a number of forms, such as,but not limited to, a network interface card, a modem, a wirelessnetwork card, a cellular network card, and the like. Indeed, thefinancial services computer 130 can receive deposit predictioninformation from the service provider computer 120 via networkinterface(s) 137. It will be appreciated that financial servicescomputer 130 may be implemented on a particular machine, which mayinclude a computer that is designed, customized, configured, orprogrammed to perform at least one or more functions of the financialapplication module 133, according to an example embodiment of theinvention.

The network 140 may include any telecommunication and/or data network,whether public, private, or a combination thereof, including a localarea network, a wide area network, an intranet, an internet, theInternet, intermediate hand-held data transfer devices, a publiclyswitched telephone network (“PSTN”), a cellular network, and/or anycombination thereof and may be wired and/or wireless. The network 140may also allow for real-time, off-line, and/or batch transactions to betransmitted between or among the financial institution computer 110, theservice provider computer 120, and/or the financial services computer130. Due to network connectivity, various methodologies as describedherein may be practiced in the context of distributed computingenvironments. It will also be appreciated that the network 140 mayinclude a plurality of networks, each with devices such as gateways androuters for providing connectivity between or among networks 140.Instead of, or in addition to, a network 140, dedicated communicationlinks may be used to connect the various devices in accordance with anexample embodiment invention.

Generally, each of the memories and data storage devices, such as thememories 112, 122, 132 and the databases 119, 129, 139, and/or any othermemory and data storage device, can store data and information forsubsequent retrieval. In this manner, the system 100 can store variousreceived or collected information in memory or a database associatedwith one or more financial institution computers 110, service providercomputers 120, and/or financial services computers 130. The memories anddatabases can be in communication with each other and/or otherdatabases, such as a centralized database, or other types of datastorage devices. When needed, data or information stored in a memory ordatabase may be transmitted to a centralized database capable ofreceiving data, information, or data records from more than one databaseor other data storage devices. In other embodiments, the databases showncan be integrated or distributed into any number of databases or otherdata storage devices. In an example embodiment, for security, thefinancial institution computer 110, service provider computer 120, andfinancial services computer 130 may have a dedicated connection torespective databases 119, 129, 139, as shown; though, in otherembodiments, the financial institution computer 110, service providercomputer 120, and financial services computer 130 or any other entitymay communicate with the databases 119, 129, 139, or another databasevia a network 140.

Suitable processors, such as the processors 116, 126, 136 of thefinancial institution computer 110, service provider computer 120,and/or financial services computer 130, respectively, may comprise amicroprocessor, an ASIC, and/or state machine. Example processors can bethose provided by Intel Corporation (Santa Clara, Calif.), AMDCorporation (Sunnyvale, Calif.), and Motorola Corporation (Schaumburg,Ill.). Such processors comprise, or may be in communication with media,for example computer-readable media, which stores instructions that,when executed by the processor, cause the processor to perform theelements described herein. Embodiments of computer-readable mediainclude, but are not limited to, an electronic, optical, magnetic, orother storage or transmission device capable of providing a processorwith computer-readable instructions. Other examples of suitable mediainclude, but are not limited to, a floppy disk, pen drive, CD-ROM, DVD,magnetic disk, memory chip, ROM, RAM, a configured processor, alloptical media, all magnetic tape or other magnetic media, or any othermedium from which a computer processor can read instructions. Also,various other forms of computer-readable media may transmit or carryinstructions to a computer, including a router, gateway, private orpublic network, or other transmission device or channel, both wired andwireless. The instructions may comprise code from anycomputer-programming language, including, but not limited to, assembly,C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, GPSS, LISP,SAS.

The system 100 shown in and described with respect to FIG. 1A isprovided by way of example only. Numerous other operating environments,system architectures, and device configurations are possible. Othersystem embodiments can include fewer or greater numbers of componentsand may incorporate some or all of the functionality described withrespect to the system components shown in FIG. 1A. As an example, theremay be an additional computer operable with the system 100 of FIG. 1A,where the computer may provide software updates, including performanceenhancement updates, to the deposit prediction module 123. According toanother example, the service provider computer 120 may be implemented bya plurality of computers, including a first service provider computer120 a and a second service provider computer 120 b, as illustrated byFIG. 1B. Indeed, as shown in FIG. 1B, there may be a first depositprediction module 123 a and a second deposit prediction module 123 b.For redundant or parallel processing, the first deposit predictionmodule 123 a may be substantially the same as the second depositprediction module 123 b. For distributed processing, the functionalitydeposit prediction module 123 for FIG. 1A may be provided or dividedaccording to the first deposit prediction module 123 a and the seconddeposit prediction module 123 b. It will be appreciated that theredundant or parallel processing, and/or the distributed processing,illustrated by FIG. 1B can likewise be similarly applied to thefinancial institution computer 110 and the financial services computer130.

Moreover, it will be appreciated that while FIG. 1A illustrates thefinancial institution computer 110, the service provider computer 120,and the financial services computer 130 as distinct computers, thefunctionality of two or more of those computers may be combined. Forexample, all of the deposit information module 113, the depositprediction module 123, and the financial application module 133 may beprovided by a single computer. According to another example, the depositinformation module 113 and the financial application module 133 may beprovided by a first computer while the deposit prediction module 123 maybe provided by a second computer. According to yet another example, thedeposit information module 113 and the deposit prediction module 123 maybe provided by a first computer while the financial application module133 may be provided by a second computer. Many variations are availablewithout departing from example embodiments of the invention.

II. Operational Overview

Embodiments of the invention may provide for deposit prediction basedupon available deposit information. The deposit information may includeelectronic deposit information as well as non-electronic depositinformation. Examples of electronic deposit information may include, butare not limited to, Automated Clearing House (ACH) deposits. Examples ofnon-electronic deposits may include, but are not limited to, deposits(e.g., paper deposits) made at a teller, ATM, lockbox, or after hourdeposit facility, where deposit amounts may include an aggregate of oneor more checks, currencies, or coins.

As will be described herein, there may be at least two types of depositprediction methodologies utilized in accordance with example embodimentsof the invention—(1) template matching or (2) Monte Carlo analysis.Template matching may be utilized to provide, but is not limited to,deposit prediction based upon at least electronic deposit informationwhile the Monte Carlo analysis may be utilized to provide, but is alsonot limited to, deposit prediction based upon at least non-electronicdeposit information. However, it will be appreciated that in otherexample embodiments, the template matching may also be applied tonon-electronic deposit information as well as a combination ofelectronic and non-electronic deposit information as well. Likewise, itwill be appreciated that Monte Carlo analysis may likewise be applied toelectronic deposit information as well as a combination of electronicand non-electronic deposit information without departing from exampleembodiments of the invention. Moreover, it will be appreciated thatother types of analysis methods, including regressions and geneticalgorithms, may likewise be utilized instead of or in combination withthe template matching or Monte Carlo Analysis described herein.

A. Template Matching

FIG. 2 illustrates a block diagram of an example deposit predictionprocess based upon template matching, according to an example embodimentof the invention. The operation of the block diagram of FIG. 2 will bediscussed in conjunction with the flow diagram of FIG. 3.

Turning now to block 302, a deposit information module 113 may access orreceive deposit information of at least one customer, including demanddeposit account (DDA) information for the at least one customer.According to an example embodiment of the invention, the depositinformation may be accessed or received from database 119 or memory 112.In block 304, the deposit information module 113 may filter or otherwiseprocess the received deposit information to obtain electronic depositinformation of the at least one customer. Likewise, the depositinformation module 113 may filter the deposit information to obtainelectronic deposit information that falls within one or more dateparameters—for example, based upon one or more date ranges (e.g., withinthe last 3 months, 6 months, 12 months, or between 2 dates) or statementperiods.

In block 306, the deposit information module 113 associated withfinancial institution computer 110 or another computer may deliver theelectronic deposit information 202 for one or more accounts of at leastone customer to the deposit prediction module 123 of service providercomputer 120 or another computer. Where the deposit information module113 and deposit prediction module deposit prediction module 123 areprovided for in the same computer, the delivery of the electronicdeposit information 202 may be an internal delivery. Otherwise, thedelivery of the electronic deposit information 202 may be delivered tothe deposit prediction module 123 via a network such as network 140. Inan example embodiment of the invention, the deposit information 202 maybe provided in a file format (e.g., CSV file, tab separated file, etc.),a database format, or any other format.

Table I illustrates example deposit information 202 for an account of anexample customer that may be delivered from the deposit informationmodule 113 to the deposit prediction module 123. As shown in Table I,the deposit information 202 may include a Date field, an Amount field,and a Count field. Where two or more electronic deposits are receivedfor an account on the same Date, the Count field may indicate the numberof electronic deposits that have been aggregated in the Amount. Forexample, in Table I, the example electronic deposit on 08-29-YYYYincludes 2 counts covering two separate deposits on the same day thatamount to $1600.00.

TABLE I Date Amount Count 08-01-YYYY $500.00 1 08-08-YYYY $600.00 108-15-YYYY $700.00 1 08-22-YYYY $600.00 1 08-29-YYYY $1600.00 209-05-YYYY $600.00 1 09-12-YYYY $700.00 1 09-19-YYYY $500.00 109-26-YYYY $1500.00 2

According to an example embodiment of the invention, the depositinformation 202 may exclude the payor name or other identificationassociated with the payor for each deposit in the deposit information202. As an example, the deposit information 202 may exclude the payorname in order to protect the privacy of its customers or to otherwisesimplify the amount or type of deposit information delivered to thedeposit prediction module 123. However, it will be appreciated that inother example embodiments of the invention, the payor name oridentification may be available as well.

In block 308, the deposit prediction module 123 may utilize templatematching to identify one or more deposit patterns in predicting a nextdeposit date and/or amount, according to an example embodiment of theinvention. In general, template matching may involve determining whichtemplate pattern, or combination of template patterns, bestcharacterizes (e.g., a best fit) or explains the received depositinformation 202. According to an example embodiment of the invention,each template pattern may define a respective time or date intervalbetween expected deposits.

FIGS. 4A and 4B illustrate example template patterns that may beutilized by the deposit prediction module 123, according to an exampleembodiment of the invention. As shown in FIGS. 4A and 4B, the templatepatterns may include weekly, biweekly (odd or even), semi-monthly,monthly, or quarterly template patterns. It will be appreciated thateach of the template patterns shown in FIGS. 4A and 4B are illustrativeof one of a family of template patterns. For example, there may be afamily of weekly template patterns, each starting on a respective day ofthe week. It will be appreciated that other template patterns, includingsemi-annual and annual template patterns, and variations thereof mayalso be available without departing from example embodiments of theinvention.

Still referring to block 308, the deposit prediction module 123 mayperform template matching in accordance with one or more triggers 220and/or one or more parameters 222. The one or more triggers 220 may beoperative to determine when the deposit prediction module 123 initiates,directs, or performs the template matching. The one or more parameters222 may determine how the template matching operates. The one or moretriggers 220 and parameters 222 may be set or specified by a serviceprovider, financial institution, financial services company, or anothersimilar entity.

The triggers 220 may determine when or a frequency that the depositprediction module 123 performs the template matching. For example, atrigger 220 may be based upon an event such as the receipt of thedeposit information 202 from the deposit information module 113 of thefinancial institution computer 110. Another trigger 220 may be aspecific request received from a financial institution, serviceprovider, financial service company, or other similar entity. Yetanother trigger 220 may specify that the deposit prediction module 123performs template matching on a periodic basis, perhaps nightly onweekday nights, according to an example embodiment of the invention.Other variations of triggers 220 are available in accordance withexample embodiments of the invention.

The parameters 222 may determine how template matching is performed bythe deposit prediction module 123. Examples of the parameters 222 mayinclude:

-   -   Maximum_Tolerance_Days: A maximum allowed distance or interval        (e.g., calendar days or business days) between a scheduled        deposit date (or adjusted deposit date) in a template pattern        and an actual deposit date. Each type of template pattern (e.g.        weekly, biweekly, etc.) can have its own value of        Maximum_Tolerance_Days.    -   Minimum_Number_Deposit_Days_Covered: A minimum number of        scheduled deposit dates (or adjusted deposit dates) that must        correspond to an actual deposit date for the set of those dates        to be considered as matching a template pattern. Each type of        template pattern (e.g. weekly, biweekly, etc.) can have its own        value of Minimum_Number_Deposit_Days_Covered.    -   Minimum_Percent_Deposit_Days_Covered: The minimum percent of        scheduled deposit dates (or adjusted deposit dates) that must        cover an actual deposit date for the set of those dates to be        considered as matching a template pattern. Each type of template        pattern (e.g. weekly, biweekly, etc.) can have its own value of        Minimum_Percent_Deposit_Days_Covered.    -   Allow_Missed_Last_Deposit?: Whether or not a pattern can end        with a missed deposit and still be regarded as matching a        template pattern.    -   Expandable_Tolerance_Pattern_Types: List of those pattern types        where Maximum_Tolerance_Days may be increased by one in a second        run of the pattern detection algorithm.    -   Interruption_Types: One of (Not_Interrupted,        Properly_Interrupted, Badly_Interrupted). Based on a pattern's        interruption type, an attempt may be made to split it into two        or more template patterns of other types.    -   RMS_Split Multiplier: RMS (Root Mean Square) error provides a        rough estimate for the goodness of fit between a template        pattern and the underlying raw data it is presumed to cover.        When the coverage of a set of more coarse-grained patterns        improves the coverage of a single more fine-grained pattern by        RMS_Split_Multiplier, a split is attempted.    -   Minimum_History_Length: The minimal number of deposits the        deposit information of an account must have on a given channel        to be considered for template matching. (Channels={Electronic        (e.g., ACH), Non-electronic (e.g., paper)}).

Once the deposit prediction module 123 has identified (or notidentified) one or more deposit patterns in block 304, then processingmay proceed to block 310. In block 310, the deposit prediction module123 may generate deposit prediction information 204. The generateddeposit prediction information 204 may identify or otherwise indicatewhich template pattern(s) were matched. The deposit predictioninformation 204 may also include information regarding one or more datesand/or amounts regarding predicted future deposits. Alternatively, if nomatching template patterns were identified in block 306, then thedeposit prediction information 204 may indicate that no matchingtemplate patterns were identified based upon the deposit information 202of the customer.

In block 312, the generated deposit prediction information 204 may bedelivered from the deposit prediction module 123 of the service providercomputer 120 or another computer to the financial application module 133of the financial services computer 130 or another computer. Where thedeposit prediction module 123 and the financial application module 133are provided for in the same computer, the delivery of the depositprediction information 204 may be an internal delivery. Otherwise, thedelivery of the deposit prediction information 204 may be delivered tothe financial application module 133 via a network such as network 140.The financial application module 133 may set/determine parameters for orotherwise provide, initiate, direct, or offer one or more services basedupon the deposit prediction information 204. According to an exampleembodiment of the invention, the services may include or be associatedwith one or more of:

-   -   Determining when to re-present returned deposit item (e.g.,        checks) to a financial institution.    -   Determining capacity to repay;    -   Setting an account overdraft limit;    -   Determining loan or line of credit eligibility;    -   Determining loan or line of credit amount;    -   Setting customized disclosure terms for loans or lines of credit        (e.g., payment due dates for loans or lines of credit);    -   Determining when to step down or shut down loans or lines of        credit;    -   Optimizing collection effort timing: When best, relative to an        upcoming or just received deposit, to contact a customer and        pursue various types of collection efforts;    -   Determining likelihood of charge-off of a loan or line of        credit;        -   A likelihood of chargeoff may be determined based upon            whether there are one or more matching template patterns,        -   A likelihood of chargeoff may be determined based upon the            length of the periodicity patterns of the one or more            template patterns, or        -   A likelihood of chargeoff may be determined base upon a            template pattern interruption;    -   Setting loan repayment terms on past-due accounts.

Many variations of FIG. 3 are available in accordance with exampleembodiments of the invention. According to one example variation, block308 may also identify an interruption of an expected deposit. Forexample, a deposit prediction module may have analyzed prior depositinformation and previously predicted one or more dates and amounts forone or more future deposits. Accordingly, one or more deposits may beexpected on the predicted date(s). The deposit prediction module canthen determine, based upon updated deposit information, whether anyactual deposits corresponding to the expected deposits were actuallyreceived. If an actual deposit was not received by the expected date, orif the amount of the actual deposit differs (e.g., differs by a certainamount) from the expected amount, the generated deposit predictioninformation may indicate an interruption in a deposit pattern. Theinterruption in the deposit pattern may be utilized to determine whetherto provide a loan, whether to allow an overdraft, or how to handlecollections of an overdue payment. Other application of this depositprediction information may be available in accordance with exampleembodiments of the invention.

An example process for identifying one or more deposit patternsaccording to block 308 of FIG. 3 will now be described with respect tothe example flow diagram of FIG. 5. It will be appreciated that the flowdiagram of FIG. 5 is provided by way of example only, and that manyvariations of FIG. 5 are available in accordance with other exampleembodiments of the invention.

In block 502 of FIG. 5, a plurality of template patterns may begenerated, retrieved, or otherwise received by the deposit predictionmodule 123. As an example, the template patterns may include weeklytemplate patterns, bi-weekly template patterns, semi-monthly templatepatterns, quarterly template patterns, and the like. Further, thetemplate patterns may start on given days of the week, days of themonth, or certain number of days from the end of a month or another dayduring the month. For example, the weekly template patterns may include5 respective template patterns, where each respective weekly templatepattern starts on a respective day from Monday-Friday. Other weeklytemplate patterns may include a Saturday weekly template pattern and aSunday weekly template pattern. Similarly, the other template patterns,including the bi-weekly template patterns, semi-monthly templatepatterns, and quarterly template patterns may each comprise one or moretemplate patterns having different start days (e.g., on a different dayof a week, month, etc.). Still referring to block 502, the depositprediction module 123 may adjust the template patterns based upon knownweekend and/or holidays. As an example, an expected date of deposit in atemplate pattern that falls on a weekend or holiday may be moved eitherforward or backward to a valid processing day.

In block 504, a template pattern from the plurality of template patternsis selected by the deposit prediction module 123. In an exampleembodiment of the invention, the deposit prediction module 123 mayprioritize the plurality of templates from most-frequent intervals(e.g., weekly template patterns) to least-frequent intervals (e.g.,monthly template patterns). However, other prioritizations may beutilized for the plurality of template patterns as well, including fromleast-frequent intervals to most-frequent intervals. The depositprediction module 123 may select one of the template patterns, perhaps atemplate pattern with the most frequent intervals (e.g., weekly templatepattern).

In block 506, the deposit prediction module 123 may apply the selectedtemplate pattern to the deposit information, which may includeelectronic deposit information comprising deposit dates and respectivecounts/amounts. Example representations of electronic depositinformation are shown in Table I above and in the diagram of FIG. 6A.

In block 508, the deposit prediction module 123 may determine whetherthe selected template pattern matches at least a portion of the depositinformation. In particular, the deposit prediction module 123 mayutilize one or more parameters 222 to determine whether there is a matchwith the selected template pattern. As an example, a parameter 222 suchas Maximum_Tolerance_Days may indicate a maximum allowed distance orinterval between a scheduled/adjusted deposit date according to thetemplate pattern and an actual deposit date according to the depositinformation. Another parameter 222 such asMinimum_Number_Deposit_Days_Covered may indicate a minimum number ofscheduled deposit dates (or adjusted deposit dates) that must cover anactual deposit date for the set of those dates to be considered apattern that matches the selected template pattern. Similarly, aparameter 222 such as Allow_Missed_Last_Deposit? may indicate whether apattern can end with a missed deposit and still be regarded as matchingthe selected template pattern. It will be appreciated that otherparameters 222 may likewise be utilized by the deposit prediction module123 without departing from example embodiments of the invention.

If block 508 determines that the selected template pattern matches atleast a portion of the deposit information, then processing may proceedto block 510. In block 510, the deposit prediction module 123 may recordor otherwise store at least an identification of the matching templatepattern. In addition, the deposit prediction module 123 may also storeinformation about non-ambiguous deposits. For example, referring to FIG.6A, a selected weekly pattern may match non-ambiguously to deposits onthe following dates where there is only a single count: 8/1, 8/8, 8/15,8/22, 9/5, 9/12, 9/19, 9/26. Based upon the non-ambiguous deposits, thedeposit prediction module 123 may perform one or more calculations,including determining an average or mean amount of the non-ambiguousdeposits, which may be stored or recorded in conjunction with theidentified matching template pattern.

Following block 510, processing may proceed to block 512. In block 512,the deposit prediction module 123 may reduce the counts in the depositinformation for deposits that match the selected template pattern. Inparticular, non-ambiguous deposits having only a single count may beeliminated from the deposit information. With respect to ambiguousdeposits where the respective counts are greater than 1, the respectivecounts in the deposit information may be reduced by 1. Further, theaggregate amount associated with each ambiguous deposit in the depositinformation may be reduced by an amount that may be determined basedupon an average, mean, median, or mode amount of the non-ambiguousdeposits. For example, in FIG. 6A, the mean amount of the non-ambiguousdeposits (8/1, 8/8, 8/15, 8/22, 9/5, 9/12, 9/19, 9/26) is $600.Accordingly, in FIG. 6B, the aggregate amount of each ambiguous depositin the deposit information has been reduced by the mean amount of $600determined for the non-ambiguous deposits. Likewise, as shown in FIG.6B, the counts for the ambiguous deposits on 8/29 and 9/26 in FIG. 6Ahave been reduced from 2 to 1.

Block 514 is reached following block 512 or if there is no match inblock 508. Block 514 may determine whether the deposit information, asmodified according to any matching template patterns, includes anyremaining deposits that have not been accounted for. For example, FIG.6B illustrates remaining counts on 8/29 and 9/26 that represent depositsthat have not been accounted for. If block 514 determines that there areany remaining deposits that have not been accounted for, then processingmay return to block 504 in which a next template pattern is selected.The deposit prediction module 123 may then repeat the process until alldeposits have been accounted for, or until all templates have beenattempted without accounting for all of the deposits.

It will be appreciated that the process of FIG. 5 may be performed anumber of instances by the deposit prediction module 123 in order todetermine a plurality of candidate sets of matching templates, accordingto an example embodiment. For example, by utilizing the process of FIG.5 multiple times, the deposit prediction module 123 may determine apossible first candidate set using a weekly template pattern and apossible second candidate set using a combination of a bi-weekly (odd)template pattern and a bi-weekly (even) template pattern. The depositprediction module 123 may then calculate an error (e.g., a standarderror, root mean square error, etc.) for each candidate set to selectthe candidate set that minimizes the error.

B. Monte Carlo Analysis

FIG. 7 illustrates a block diagram of an example deposit predictionprocess based upon Monte Carlo analysis, according to an exampleembodiment of the invention. The operation of the block diagram of FIG.7 will be discussed in conjunction with the flow diagram of FIG. 8.

Turning now to block 802 of FIG. 8, a deposit information module 113 mayaccess or receive deposit information for at least one customer,including demand deposit account (DDA) information for the at least onecustomer. According to an example embodiment of the invention, thedeposit information may be accessed or received from database 119 ormemory 112. In block 804, the deposit information module 113 may filteror otherwise process the received deposit information to obtain at leastnon-electronic deposit information for the at least one customer. Asdescribed herein, non-electronic deposits may include ATM orteller-initiated deposits in the form of paper instruments such aschecks or currencies such as cash. Likewise, the deposit informationmodule 113 may filter the deposit information to obtain non-electronicdeposit information that falls within one or more date parameters—forexample, based upon one or more date ranges (e.g., within the last 3months, 6 months, 12 months, or between 2 dates) or statement periods.

In block 806, the deposit information module 113 associated withfinancial institution computer 110 or another computer may deliver atleast the non-electronic deposit information 702 for at least onecustomer to the deposit prediction module 123 of service providercomputer 120 or another computer. Where the deposit information module113 and deposit prediction module deposit prediction module 123 areprovided for in the same computer, the delivery of the non-electronicdeposit information 702 may be an internal delivery. Otherwise, thedelivery of the non-electronic deposit information 702 may be deliveredto the deposit prediction module 123 via a network such as network 140.In an example embodiment of the invention, the deposit information 702may be provided in a file format (e.g., CSV file, tab separated file,etc.), a database format, or any other format.

Table II illustrates example non-electronic deposit information 702 foran example customer that may be delivered from the deposit informationmodule 113 to the deposit prediction module 123. As shown in Table II,the deposit information 702 may include a Date field, an Amount field,and a Count field.

TABLE II Date Amount 01-22-YYYY $800.00 02-12-YYYY $100.00 03-11-YYYY$100.00 03-25-YYYY $1400.00 04-22-YYYY $3200.00 05-13-YYYY $100.0006-24-YYYY $1100.00

According to an example embodiment of the invention, the depositinformation 702 may include an aggregate of all non-electronic depositsmade on a particular date. For example, referring to Table II, thedeposit on 03-25-YYYY may actually represent a plurality of checks thatwere deposited at a Teller or ATM. In addition, the deposit information702 may exclude the payor name or other identification associated withthe payor for each deposit in the deposit information 702. As anexample, the deposit information 702 may exclude the payor name in orderto protect the privacy of its customers or to otherwise simplify theamount or type of deposit information delivered to the depositprediction module 123. However, it will be appreciated that in otherexample embodiments of the invention, the payor name or identificationmay be available as well.

In block 808, the deposit prediction module 123 may utilize Monte Carloanalysis to identify the largest expected total dollar or monetaryamount that can, with a given statistical confidence, be expected to bedeposited within a given interval or date range, according to an exampleembodiment of the invention. It will be appreciated that the largestexpected total dollar or monetary amount does not preclude actual totaldollar or monetary amounts that exceed the largest expected total dollaror monetary amount. In general, Monte Carlo analysis may use historicaldistributions in gaps between deposits and amounts to determineprobabilities with which future deposits are likely to occur.

Still referring to block 808, the deposit prediction module 123 mayperform the Monte Carlo analysis in accordance with one or more triggers720 and/or one or more parameters 722. The one or more triggers 720 maybe operative to determine when the deposit prediction module 123initiates, directs, or performs the Monte Carlo analysis. The one ormore parameters 722 may determine how the Monte Carlo analysis operates.The one or more triggers 720 and parameters 722 may be set or specifiedby a service provider, financial institution, financial servicescompany, or another similar entity.

The triggers 720 may determine when or a frequency with which thedeposit prediction module 123 performs the Monte Carlo analysis. Forexample, a trigger 720 may be based upon an event such as the receipt ofthe deposit information 702 from the deposit information module 113 ofthe financial institution computer 110. Another trigger 720 may be aspecific request received from a financial institution, serviceprovider, financial service company, or other similar entity. Yetanother trigger 720 may specify that the deposit prediction module 123performs the Monte Carlo analysis on a periodic basis, perhaps nightlyon weekday nights, according to an example embodiment of the invention.Other variations of triggers 720 are available in accordance withexample embodiments of the invention.

The parameters 722 may determine how Monte Carlo analysis is performedby the deposit prediction module 123. Examples of the parameters 722 mayinclude:

-   -   Max_Number_Iterations: A maximum number of iterations of the        Monte Carlo analysis to run for a given account. In an example        embodiment of the invention, the Max_Number_Iterations can be        used to halt the Monte Carlo analysis if convergence does not        occur by the Max_Number_Iterations.    -   Simulation_Period: The duration (e.g., in days) of the period        for which deposits are being simulated. It will be appreciated        that too long a period may result in highly speculative results        or excessive computer processing.    -   Confidence_Level: The desired statistical confidence level to be        utilized with the deposit prediction in accordance with the        Monte Carlo analysis. An example Confidence_Level may be 90% or        0.90.    -   Outlier_Multiplier: When the largest deposit amount is        Outlier_Tolerance times as big as the next largest amount, it        may be filtered out as an outlier.    -   Max_Allowed_Gap: Any series of deposits preceding a gap in days        greater than or equal to Max_Allowed_Gap may be filtered out of        the data.    -   Min_Weekly_Amount: Accounts having respective deposit        information (i.e., case histories) in which the average weekly        deposit is less than Min_Weekly_Amount may be dropped from        consideration by the Monte Carlo analysis.

Min_Total_Amount: Accounts having respective deposit information wherethe total deposit amount is less than Min_Total_Amount may be droppedfrom consideration by the Monte Carlo analysis.

Min_Case_History_Length: Accounts have respective deposit informationfor a time period (e.g., in days) less than Min_Case_History_Length maybe dropped from consideration by the Monte Carlo analysis.

-   -   Min_Deposits_Per_Month: Accounts having respective deposit        information that average fewer than Min_Deposits_Per_Month may        be dropped from consideration by the Monte Carlo analysis.    -   Max_Deposits_Per_Month: Accounts having respective deposit        information that average greater than Max_Deposits_Per_Month may        be dropped from consideration by the Monte Carlo analysis.    -   Filter_Sequence: An ordering in which the filtering and/or        dropping criteria are evaluated or applied. It will be        appreciated that depending on the order in which the parameters        are evaluated, different accounts might survive or be dropped        for consideration, and a surviving account might have deposit        information containing a different set of deposits.

Once the deposit prediction module 123 has identified (or notidentified) the largest expected total dollar or monetary amount thatcan, with a given statistical confidence, be expected to be depositedwithin a given interval or date range, then processing may proceed toblock 810. In block 810, the deposit prediction module 123 may generatedeposit prediction information 704. The generated deposit predictioninformation 704 may identify or otherwise indicate the determinedlargest dollar or monetary amount, the date by which the largest dollaror monetary amount can be expected, and any statistical confidencelevels that the foregoing may be subject to. Alternatively, the depositprediction information 704 may indicate that the Monte Carlo analysiscould not make a determination of the largest expected total dollar ormonetary amount that can, with a given statistical confidence, beexpected to be deposited within a given interval or date range.

In block 812, the generated deposit prediction information 704 may bedelivered from the deposit prediction module 123 of the service providercomputer 120 or another computer to the financial application module 133of the financial services computer 130 or another computer. Where thedeposit prediction module 123 and the financial application module 133are provided for in the same computer, the delivery of the depositprediction information 704 may be an internal delivery. Otherwise, thedelivery of the deposit prediction information 704 may be delivered tothe financial application module 133 via a network such as network 140.The financial application module 133 may set/determine parameters for orotherwise provide, initiate, direct, or offer one or more services basedupon the deposit prediction information 704. According to an exampleembodiment of the invention, the services may include or be associatedwith one or more of:

-   -   Determining when to re-present returned deposit item (e.g.,        checks) to a financial institution.    -   Determining capacity to repay;    -   Setting an account overdraft limit;    -   Determining loan or line of credit eligibility;    -   Determining loan or line of credit amount;    -   Setting customized disclosure terms for loans or lines of credit        (e.g., payment due dates for loans or lines of credit);    -   Determining when to step down or shut down loans or lines of        credit;    -   Optimizing collection effort timing: When best, relative to an        upcoming or just received deposit, to contact a customer and        pursue various types of collection efforts;    -   Determining likelihood of charge-off of a loan or line of        credit;    -   Setting loan repayment terms on past-due accounts.

An example process for an example Monte Carlo analysis according toblock 808 of FIG. 8 will now be described with respect to the exampleflow diagram of FIG. 9. It will be appreciated that the flow diagram ofFIG. 9 is provided by way of example only, and that many variations ofFIG. 9 are available in accordance with other example embodiments of theinvention. It will be also appreciated that the process illustrated inFIG. 9 is only for a single iteration of the Monte Carlo analysis, andthat many iterations may be necessary to determine the largest expectedtotal dollar or monetary amount that can, with a given statisticalconfidence, be expected to be deposited within a given interval or daterange. For example, the Monte Carlo analysis may be used to answer thequestion: “What is the largest total amount we expect to be deposited tothe account during period (T_(min), T_(max)) with a particularconfidence level (e.g., perhaps set by the parameter Confidence_Level(e.g., 0.90))?”

In block 902 of FIG. 9, the deposit prediction module 123 may set orinitialize a plurality of variables. For example, the deposit predictionmodule 123 may set or initialize the following variables:

-   -   N=a number of deposits,    -   S_(i)=an amount of the i-th deposit,    -   T_(i)=date of the i-th deposit,    -   M_(i)=T_(i)−T_(i−1), which represents the interval (in days)        between the (i−1)-th deposit and the i-th deposit, and    -   T_(end)=the last date of the account history, where T_(end) is        greater than or equal to T_(N),    -   T_(max)=a stop date for the target date range,    -   T_(min)=a start date for the target date range,    -   j=0.

In block 904, the deposit prediction module 123 may find the date of thenext deposit after date T_(N). To do this, the deposit prediction module123 may select a random number k uniformly distributed on {2, . . . ,N}. The date of the next deposit may be designated as dateT_(N+j+1)=T_(N+j)+M_(k). In block 906, if date T_(N+1) is less or equalto date T_(min), then Counter j may be incremented in block 907, anddate T_(N+1) may be chosen again in block 904, according to an exampleembodiment of the invention.

Following block 906, processing may proceed to block 908. In block 908,the deposit prediction module 123 may determine whether T_(N+j−1) isless than T_(max) such that T_(N+j+)1 falls within the date range orperiod (T_(min), T_(max)). If so, then processing may proceed to block910. In block 910, an amount of deposit on T_(N+j+1) may be determinedby choosing randomly with the uniform distribution from {S₁, . . . ,S_(N)}. The completion of block 910 may complete a first iteration ofthe Monte Carlo analysis.

For a particular iteration of the Monte Carlo analysis, blocks 904-910may be repeated to obtain a distribution function of the expectedlargest amount deposited during the period or date range or period(T_(min), T_(max)). For example, a second iteration may proceed todetermine the date and amount of the (N+2)-th deposit. The process forthe particular iteration may be terminated when the date of the currentdeposit becomes greater than the date T_(max).

It will be appreciated that the Monte Carlo analysis may be repeated anumber of iterations until a distribution function of the expectedlargest total amount deposited during the date range or period (T_(min),T_(max)) is obtained within a desired confidence level. It will beappreciated that in some instances, there may be no convergence to anexpected largest total amount deposited during the date range or period(T_(min), T_(max)) for a desired confidence level.

FIG. 9 illustrates a graphical representation illustrating an examplesingle Monte Carlo iteration according to an example embodiment of theinvention. As shown by 902, a random amount and interval may be chosenfrom the available deposit information, extending the deposit streaminto the future, although not yet within target date range {T_(min), . .. , T_(max)}. In 904, a second random amount and interval may be chosenfrom the available deposit information, extending the deposit streamfurther into the future, although again not yet within target date range{T_(min), . . . , T_(max)}. In 906, the deposit stream eventuallyextends into the target date range {T_(min), . . . , T_(max)}. In 908,the deposit stream extends further into the target date range {T_(min),. . . , T_(max)}. In 910, the deposit stream extends yet further intothe target date range {T_(min), . . . , T_(max)}. In 912, the depositstream finally extends beyond the target date range {T_(min), . . . ,T_(max)}. A distribution 1014 of the amounts deposited during period(T_(min), T_(max)) is then obtained, for a confidence level, accordingto an example embodiment invention.

FIG. 10 illustrates a graphical representation illustrating a pluralityof distributions 1102 a-n of expected amounts deposited during thetarget date range corresponding to a respective plurality of Monte Carloiterations a-n, according to an example embodiment of the invention. Itwill be appreciated that the number of Monte Carlo iterations that areperformed may be a predetermined number. Alternatively, the Monte Carloiterations can continue until statistical measures (e.g., standarderror, standard deviation, etc.) indicate that the results (e.g., adistribution function based upon the aggregate of the distributions 1102a-n) accumulated so far as stable and unlikely to change with furtheriterations. Indeed, the Monte Carlo analysis may be repeated a number ofiterations until the current distribution function results in theidentification of in a largest monetary amount that can, within a givenor predetermined confidence level, be expected on or before a givendate. For example, each iteration of the Monte Carlo analysis mayfurther refine a distribution function associated with a statisticalmeasure such a standard error until the largest monetary amount isdetermined with a desired standard error.

The invention is described above with reference to block and flowdiagrams of systems, methods, apparatuses, and/or computer programproducts according to example embodiments of the invention. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented, or may not necessarily need to be performed at all, accordingto some embodiments of the invention.

In certain embodiments, performing the specified functions, elements orsteps can transform an article into another state or thing. Forinstance, example embodiments of the invention can provide certainsystems and methods that transform deposit information representative ofactual historical deposits into an account into a deposit predictioninformation representative of a next deposit and/or amount.

These computer-executable program instructions may be loaded onto ageneral purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flowchart blockor blocks. These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks. As an example, embodiments of the invention may provide for acomputer program product, comprising a computer usable medium having acomputer readable program code or program instructions embodied therein,said computer readable program code adapted to be executed to implementone or more functions specified in the flow diagram block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational elements or steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide elements or steps for implementing the functionsspecified in the flow diagram block or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specified functionsand program instruction means for performing the specified functions. Itwill also be understood that each block of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, can be implemented by special-purpose, hardware-based computersystems that perform the specified functions, elements or steps, orcombinations of special purpose hardware and computer instructions.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains andhaving the benefit of the teachings presented in the foregoingdescriptions and the associated drawings. Therefore, it is to beunderstood that the invention is not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A method, comprising executing computer program instructions by oneor more processors for: receiving deposit information associated with aplurality of deposits for a deposit account of a financial institutioncustomer, wherein the deposit information includes a plurality ofdeposit amounts, and a respective date associated with each of theplurality of deposit amounts; applying a Monte Carlo analysis to atleast a portion of the received deposit information; and identifying,based upon the applied Monte Carlo analysis, a largest total monetaryamount that can be expected to be deposited within a time intervalaccording to a predetermined confidence level.
 2. The method of claim 1,wherein the Monte Carlo analysis comprises a plurality of iterations toidentify the largest total monetary amount, wherein a combination of theiterations yields a distribution function that is utilized indetermining the largest total monetary amount.
 3. The method of claim 2,wherein the distribution function provides a standard error or standarddeviation for the largest total amount.
 4. The method of claim 2,wherein each iteration includes respective expected amounts depositedand time intervals between the expected amounts deposited, wherein acombination of the respective amounts deposited and time intervals isutilized to yield the distribution function.
 5. The method of claim 4,wherein the respective expected amounts deposited and the time intervalsfor each iteration are determined by the Monte Carlo analysis selectingfrom prior actual deposit amounts and time intervals between actualdeposit amounts in the at least a portion of the received depositinformation.
 6. The method of claim 5, wherein the Monte Carlo analysisrandomly selects from prior actual deposits and time intervals inaccordance with a uniform distribution.
 7. The method of claim 1,wherein the deposit information includes first deposit informationassociated with electronic deposits and second deposit informationassociated with non-electronic deposits.
 8. The method of claim 7,wherein the non-electronic deposits are associated with ATM deposits orteller-assisted deposits.
 9. The method of claim 1, wherein the portionof the received deposit information is obtained by filtering out atleast a portion of the received deposit information.
 10. The method ofclaim 9, wherein the portion of the received deposit information isobtained by filtering the deposit information according to one or moreamount criteria associated with a deposit amount being either too highor too low.
 11. The method of claim 1, wherein the identified largesttotal monetary amount is utilized in qualifying an opportunityassociated with the financial institution customer.
 12. The method ofclaim 11, wherein the opportunity is associated with (i) approving aloan or credit line for the financial institution customer; (ii)increasing or reducing an amount of the loan or credit line for thefinancial institution customer, (iii) determining a due date or paymentschedule for the loan or credit line for the financial institutioncustomer; and (iv) determining when to contact a financial institutioncustomer for collections or an offering.
 13. The method of claim 1,wherein the deposit information is received from the financialinstitution computer, and wherein at least the identified largest totalmonetary amount is provided to a financial services computer.
 14. Themethod of claim 13, wherein the financial institution computer and thefinancial services computer are (i) associated with a same entity, or(ii) are a same computer.
 15. A system, comprising: a memory that storescomputer-executable instructions; and a processor configured to accessthe memory, wherein the processor is further configured to execute thecomputer-executable instructions to: receive deposit informationassociated with a plurality of deposits for a deposit account of afinancial institution customer, wherein the deposit information includesa plurality of deposit amounts, and a respective date associated witheach of the plurality of deposit amounts; apply a Monte Carlo analysisto at least a portion of the received deposit information; and identify,based upon the applied Monte Carlo analysis, a largest total monetaryamount that can be expected to be deposited within a time intervalaccording to a predetermined confidence level.
 16. The system of claim15, wherein the Monte Carlo analysis comprises a plurality of iterationsto identify the largest total monetary amount, wherein a combination ofthe iterations yields a distribution function that is utilized indetermining the largest total monetary amount.
 17. The system of claim16, wherein the distribution function provides a standard error orstandard deviation for the largest total amount.
 18. The system of claim16, wherein each iteration includes respective expected amountsdeposited and time intervals between the expected amounts deposited,wherein a combination of the respective amounts deposited and timeintervals is utilized to yield the distribution function.
 19. The systemof claim 18, wherein the respective expected amounts deposited and thetime intervals for each iteration are determined by the Monte Carloanalysis selecting from prior actual deposit amounts and time intervalsbetween actual deposit amounts in the at least a portion of the receiveddeposit information.
 20. The system of claim 19, wherein the Monte Carloanalysis randomly selects from prior actual deposits and time intervalsin accordance with a uniform distribution.
 21. The system of claim 15,wherein the deposit information includes first deposit informationassociated with electronic deposits and second deposit informationassociated with non-electronic deposits.
 22. The system of claim 21,wherein the non-electronic deposits are associated with ATM deposits orteller-assisted deposits.
 23. The system of claim 15, wherein theportion of the received deposit information is obtained by filtering outat least a portion of the received deposit information.
 24. The systemof claim 23, wherein the portion of the received deposit information isobtained by filtering the deposit information according to one or moreamount criteria associated with a deposit amount being either too highor too low.
 25. The system of claim 15, wherein the identified largesttotal monetary amount is utilized in qualifying an opportunityassociated with the financial institution customer.
 26. The system ofclaim 25, wherein the opportunity is associated with (i) approving aloan or credit line for the financial institution customer; (ii)increasing or reducing an amount of the loan or credit line for thefinancial institution customer, (iii) determining a due date or paymentschedule for the loan or credit line for the financial institutioncustomer; and (iv) determining when to contact a financial institutioncustomer for collections or an offering.
 27. The system of claim 15,wherein the processor is configured to receive the deposit informationfrom the financial institution computer, and wherein the process isconfigured to provide at least the identified largest total monetaryamount to a financial services computer.
 28. The system of claim 27,wherein the financial institution computer and the financial servicescomputer are (i) associated with a same entity, or (ii) are a samecomputer.
 29. A system, comprising: means for receiving depositinformation associated with a plurality of deposits for a depositaccount of a financial institution customer, wherein the depositinformation includes a plurality of deposit amounts, and a respectivedate associated with each of the plurality of deposit amounts; means forapplying a Monte Carlo analysis to at least a portion of the receiveddeposit information; and means for identifying, based upon the appliedMonte Carlo analysis, a largest total monetary amount that can beexpected to be deposited within a time interval according to apredetermined confidence level.